Senior Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at G MASS. London · contract.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build tools for deploying, monitoring and alerting on models throughout their lifecycle
- Keep the Kubeflow environment running well and improve it for Data Scientists and Actuaries
- Create pricing analysis tools that speed up impact reviews and cut manual work
- Work with pricing and product teams to deliver useful tooling, explaining technical ideas clearly to varied audiences
What you bring
- A bachelor’s or master’s degree in statistics, data science, computer science or a related subject
- Strong experience overseeing models from development through batch and online production use
- You can build and deploy production-ready Python applications using pandas and scikit-learn
- Experience with Kubernetes, Docker, CI/CD and git workflows, plus familiarity with AWS and Snowflake or SQL
Who this fits
This senior contract role suits an ML engineer who can support pricing teams and work across the model lifecycle. It is based onsite in London. The contract runs for twelve months, with a possibility of extension.
From the employer
G MASS Consulting are supporting a leading Audit and Advisory business. We’re looking for a Machine Learning Engineer to shape and scale their pricing technology. In this role, you’ll design and own ML platforms that streamline pricing workflows, support rapid model deployment, and ensure models perform reliably at scale. Partnering with Data Science, Actuarial, and Product teams.
Responsibilities:
- Build and support ML lifecycle tooling for model deployment, monitoring, and alerting
- Maintain and improve the Kubeflow environment for Data Scientists and Actuaries
- Create pricing analytics tools to accelerate impact analysis and reduce manual work
- Collaborate with pricing and product teams to deliver high-impact tooling
- Communicate complex concepts clearly to technical and non-technical audiences
Requirements
- Bachelor’s or Master’s degree in Statistics, Data Science, Computer Science, or a related field
- Strong experience managing the full ML model lifecycle (batch and online)
- Solid understanding of statistical methods, including GLMs and modern ML techniques
- Proven ability to build and deploy production-quality Python applications (pandas, scikit-learn)
- Experience with DevOps and ML tooling, including Kubernetes, Docker, CI/CD, and git-based workflows
- Familiarity with cloud platforms (AWS) and cloud data warehouses (Snowflake/SQL)
Benefits
Salary: to be discussed, depending on experience
Length: 12 months, with the view to extend